Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f1be1e2fd30>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f1be1d68e10>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
        
    inputs_real   = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z      = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, None, name='learning_rate')

    return inputs_real, inputs_z, learning_rate



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def default_gan_activation(x,alpha=0.2):
    #return tf.nn.elu(x)
    return tf.maximum(alpha * x, x)
In [7]:
def discriminator_valid_padding(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    x = images
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(x, 64, 6, strides=2, padding='valid')
        relu1 = default_gan_activation(x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 4, strides=2, padding='valid')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = default_gan_activation(bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 3, strides=2, padding='valid')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = default_gan_activation(bn3)
        
        relu3_size = int(relu3.shape[1]*relu3.shape[2]*relu3.shape[3])

        flat = tf.reshape(relu3, (-1, relu3_size))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator_valid_padding, tf)
Tests Passed
In [8]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    x = images
    d = 0
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')
        x1 = tf.layers.dropout(x1,d)
        relu1 = default_gan_activation(x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.dropout(x2,d)
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = default_gan_activation(bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        x3 = tf.layers.dropout(x3,d)
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = default_gan_activation(bn3)
        
        relu3_size = int(relu3.shape[1]*relu3.shape[2]*relu3.shape[3])

        flat = tf.reshape(relu3, (-1, relu3_size))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [9]:
def generator_valid_padding(z, out_channel_dim, is_train=True,debug = False):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    if(debug):
        print('-----verify generator inputs:-----')
        [print(i) for i in zip(['z', 'out_channel_dim', 'is_train'],[z, out_channel_dim, is_train])]
    
    # TODO: Implement Function
    (z, output_dim, reuse, training) = (z, out_channel_dim, not(is_train), is_train)
    
    default_padding = 'same'
    default_kernel_size = 5

    with tf.variable_scope('generator', reuse=reuse):
        a=2
        x1 = tf.layers.dense(z, a*a*512)
        x1 = tf.reshape(x1, (-1, a, a, 512))
        x1 = tf.layers.batch_normalization(x1, training=training)
        x1 = default_gan_activation(x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 3, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=training)
        x2 = default_gan_activation(x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 4, strides=2, padding='valid')
        x3 = tf.layers.batch_normalization(x3, training=training)
        x3 = default_gan_activation(x3)
        
        logits = tf.layers.conv2d_transpose(x3, output_dim, 6, strides=2,  padding='valid')
        
        out = tf.tanh(logits)
        
        if(debug):
            print('-----verify generator processing:-----')
            [print(i) for i in zip(['x1','x2','x3'],[x1,x2,x3])]
            
            print('-----verify generator outputs:-----')
            print(out)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator_valid_padding, tf)
Tests Passed
In [10]:
def generator(z, out_channel_dim, is_train=True,debug = False):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    if(debug):
        print('-----verify generator inputs:-----')
        [print(i) for i in zip(['z', 'out_channel_dim', 'is_train'],[z, out_channel_dim, is_train])]
    
    # TODO: Implement Function
    (z, output_dim, reuse, training) = (z, out_channel_dim, not(is_train), is_train)
    
    default_padding = 'same'
    default_kernel_size = 5

    with tf.variable_scope('generator', reuse=reuse):
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=training)
        x1 = default_gan_activation(x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, default_kernel_size, strides=2, padding=default_padding)
        x2 = tf.layers.batch_normalization(x2, training=training)
        x2 = default_gan_activation(x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, default_kernel_size, strides=2, padding=default_padding)
        x3 = tf.layers.batch_normalization(x3, training=training)
        x3 = default_gan_activation(x3)
        
        logits = tf.layers.conv2d_transpose(x3, output_dim, default_kernel_size, strides=1, padding=default_padding)
        
        out = tf.tanh(logits)
        
        if(debug):
            print('-----verify generator processing:-----')
            [print(i) for i in zip(['x1','x2','x3'],[x1,x2,x3])]
            
            print('-----verify generator outputs:-----')
            print(out)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [11]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    output_dim = out_channel_dim

    g_model = generator(input_z, output_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [12]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """

    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [13]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [14]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    print('-----verify train inputs:-----')
    [print(i) for i in zip(['epoch_count', 'batch_size', 'z_dim', 'learning_rate', 'beta1', 'get_batches', 'data_shape', 'data_image_mode'],\
                           [epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode])]
    
    
    print_every=max(batch_size//16,8)
    show_every=print_every*2
    n_images = 4**2
    saver_path = './checkpoints/generator'+data_image_mode+'.ckpt'
    
    tf.reset_default_graph()

    # MODEL  INPUT
    input_real, input_z, input_learning_rate =  model_inputs(*data_shape[1:], z_dim)

    # MODEL
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[-1])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        saver = tf.train.Saver()
        
        try:
            tf.saver.restore(sess, saver_path)
        except:
            pass
        
        steps = 1
        samples, losses = [], []
        for e in range(epoch_count):
            #for x, y in dataset.batches(batch_size):
            for batch_x in get_batches(min(steps,batch_size)): ## incriasing batchsize to optimise trainingspeed (and time) over iterations, partly simular to a learning rate declay
                steps += 1
                #print(steps,end='')

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim)) 
                batch_x *= 2

                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={input_real         : batch_x,
                                                     input_z            : batch_z,
                                                     input_learning_rate: learning_rate})
                
                for _ in range(2):
                    _ = sess.run(g_train_opt, feed_dict={input_real         : batch_x,
                                                         input_z            : batch_z,
                                                         input_learning_rate: learning_rate})
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_x})
                    train_loss_g = g_loss.eval({input_z: batch_z})
 
                    print("Epoch {}/{}...".format(e+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
 
                if steps % show_every == 0:
                    show_generator_output(sess, n_images, input_z, data_shape[-1], data_image_mode)
            
                saver.save(sess, saver_path)

    with open('samples_'+data_image_mode+'.pkl', 'wb') as f:
        pkl.dump(samples, f)
    
    fig, ax = pyplot.subplots()
    losses = np.array(losses)
    pyplot.plot(losses.T[0], label='Discriminator', alpha=0.5)
    pyplot.plot(losses.T[1], label='Generator', alpha=0.5)
    pyplot.title("Training Losses")
    pyplot.legend()

    return losses, samples

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 2**6
z_dim = 2**7
learning_rate = 0.0002
beta1 =  0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
-----verify train inputs:-----
('epoch_count', 2)
('batch_size', 64)
('z_dim', 128)
('learning_rate', 0.0002)
('beta1', 0.5)
('get_batches', <bound method Dataset.get_batches of <helper.Dataset object at 0x7f1b47d1bbe0>>)
('data_shape', (60000, 28, 28, 1))
('data_image_mode', 'L')
Epoch 1/2... Discriminator Loss: 0.1297... Generator Loss: 2.4263
Epoch 1/2... Discriminator Loss: 2.5577... Generator Loss: 0.1001
Epoch 1/2... Discriminator Loss: 3.3562... Generator Loss: 0.0574
Epoch 1/2... Discriminator Loss: 2.8415... Generator Loss: 0.0768
Epoch 1/2... Discriminator Loss: 2.2778... Generator Loss: 0.1565
Epoch 1/2... Discriminator Loss: 2.4744... Generator Loss: 0.1500
Epoch 1/2... Discriminator Loss: 2.4285... Generator Loss: 0.1202
Epoch 1/2... Discriminator Loss: 2.3957... Generator Loss: 0.1482
Epoch 1/2... Discriminator Loss: 1.6974... Generator Loss: 0.2939
Epoch 1/2... Discriminator Loss: 1.7110... Generator Loss: 0.2834
Epoch 1/2... Discriminator Loss: 2.3476... Generator Loss: 0.1405
Epoch 1/2... Discriminator Loss: 2.3779... Generator Loss: 0.2029
Epoch 1/2... Discriminator Loss: 1.8695... Generator Loss: 0.2174
Epoch 1/2... Discriminator Loss: 1.4631... Generator Loss: 0.3117
Epoch 1/2... Discriminator Loss: 1.8292... Generator Loss: 0.2513
Epoch 1/2... Discriminator Loss: 1.7015... Generator Loss: 0.2792
Epoch 1/2... Discriminator Loss: 2.1000... Generator Loss: 0.2820
Epoch 1/2... Discriminator Loss: 1.8911... Generator Loss: 0.2088
Epoch 1/2... Discriminator Loss: 1.7341... Generator Loss: 0.2752
Epoch 1/2... Discriminator Loss: 1.8907... Generator Loss: 0.2266
Epoch 1/2... Discriminator Loss: 1.6766... Generator Loss: 0.3285
Epoch 1/2... Discriminator Loss: 1.7185... Generator Loss: 0.3166
Epoch 1/2... Discriminator Loss: 1.9781... Generator Loss: 0.2301
Epoch 1/2... Discriminator Loss: 1.1085... Generator Loss: 0.5235
Epoch 1/2... Discriminator Loss: 1.1473... Generator Loss: 0.5662
Epoch 1/2... Discriminator Loss: 1.2602... Generator Loss: 0.4893
Epoch 1/2... Discriminator Loss: 1.5553... Generator Loss: 0.3642
Epoch 1/2... Discriminator Loss: 1.2989... Generator Loss: 0.4741
Epoch 1/2... Discriminator Loss: 1.6347... Generator Loss: 0.3887
Epoch 1/2... Discriminator Loss: 1.7801... Generator Loss: 0.3778
Epoch 1/2... Discriminator Loss: 1.0895... Generator Loss: 0.6870
Epoch 1/2... Discriminator Loss: 0.9733... Generator Loss: 0.6942
Epoch 1/2... Discriminator Loss: 1.0215... Generator Loss: 0.6040
Epoch 1/2... Discriminator Loss: 0.3811... Generator Loss: 1.3748
Epoch 1/2... Discriminator Loss: 1.9712... Generator Loss: 0.2148
Epoch 1/2... Discriminator Loss: 2.2943... Generator Loss: 0.1796
Epoch 1/2... Discriminator Loss: 1.7429... Generator Loss: 0.4804
Epoch 1/2... Discriminator Loss: 1.6357... Generator Loss: 0.3833
Epoch 1/2... Discriminator Loss: 1.2372... Generator Loss: 0.4637
Epoch 1/2... Discriminator Loss: 1.8747... Generator Loss: 0.3200
Epoch 1/2... Discriminator Loss: 0.8803... Generator Loss: 0.8208
Epoch 1/2... Discriminator Loss: 1.5608... Generator Loss: 0.3428
Epoch 1/2... Discriminator Loss: 1.9383... Generator Loss: 0.3699
Epoch 1/2... Discriminator Loss: 1.3485... Generator Loss: 0.5673
Epoch 1/2... Discriminator Loss: 1.4796... Generator Loss: 0.4300
Epoch 1/2... Discriminator Loss: 1.4198... Generator Loss: 0.5977
Epoch 1/2... Discriminator Loss: 1.3557... Generator Loss: 0.4767
Epoch 1/2... Discriminator Loss: 1.1601... Generator Loss: 0.6007
Epoch 1/2... Discriminator Loss: 1.4561... Generator Loss: 0.4453
Epoch 1/2... Discriminator Loss: 1.7158... Generator Loss: 0.3537
Epoch 1/2... Discriminator Loss: 1.8188... Generator Loss: 0.2655
Epoch 1/2... Discriminator Loss: 1.2299... Generator Loss: 0.7306
Epoch 1/2... Discriminator Loss: 1.4691... Generator Loss: 0.4564
Epoch 1/2... Discriminator Loss: 2.0056... Generator Loss: 0.2872
Epoch 1/2... Discriminator Loss: 1.5262... Generator Loss: 0.5459
Epoch 1/2... Discriminator Loss: 1.3718... Generator Loss: 0.4778
Epoch 1/2... Discriminator Loss: 0.9465... Generator Loss: 0.6911
Epoch 1/2... Discriminator Loss: 1.7823... Generator Loss: 0.3593
Epoch 1/2... Discriminator Loss: 1.3493... Generator Loss: 0.5914
Epoch 1/2... Discriminator Loss: 1.4563... Generator Loss: 0.4896
Epoch 1/2... Discriminator Loss: 1.0089... Generator Loss: 0.6991
Epoch 1/2... Discriminator Loss: 1.1658... Generator Loss: 0.6987
Epoch 1/2... Discriminator Loss: 1.7754... Generator Loss: 0.5519
Epoch 1/2... Discriminator Loss: 1.2829... Generator Loss: 0.6624
Epoch 1/2... Discriminator Loss: 1.0947... Generator Loss: 0.6454
Epoch 1/2... Discriminator Loss: 1.7538... Generator Loss: 0.4523
Epoch 1/2... Discriminator Loss: 1.4822... Generator Loss: 0.5004
Epoch 1/2... Discriminator Loss: 0.7043... Generator Loss: 1.0882
Epoch 1/2... Discriminator Loss: 1.5590... Generator Loss: 0.3825
Epoch 1/2... Discriminator Loss: 0.9844... Generator Loss: 0.7001
Epoch 1/2... Discriminator Loss: 1.0572... Generator Loss: 0.6312
Epoch 1/2... Discriminator Loss: 1.9403... Generator Loss: 0.3366
Epoch 1/2... Discriminator Loss: 1.8222... Generator Loss: 0.3378
Epoch 1/2... Discriminator Loss: 1.6701... Generator Loss: 0.4038
Epoch 1/2... Discriminator Loss: 1.0484... Generator Loss: 0.7406
Epoch 1/2... Discriminator Loss: 1.6601... Generator Loss: 0.3373
Epoch 1/2... Discriminator Loss: 1.5320... Generator Loss: 0.4129
Epoch 1/2... Discriminator Loss: 1.3804... Generator Loss: 0.4276
Epoch 1/2... Discriminator Loss: 0.9245... Generator Loss: 0.7887
Epoch 1/2... Discriminator Loss: 2.1460... Generator Loss: 0.2528
Epoch 1/2... Discriminator Loss: 1.8837... Generator Loss: 0.4749
Epoch 1/2... Discriminator Loss: 1.6420... Generator Loss: 0.4309
Epoch 1/2... Discriminator Loss: 1.1723... Generator Loss: 0.5479
Epoch 1/2... Discriminator Loss: 0.6764... Generator Loss: 1.1300
Epoch 1/2... Discriminator Loss: 1.7567... Generator Loss: 0.4186
Epoch 1/2... Discriminator Loss: 1.1911... Generator Loss: 0.7431
Epoch 1/2... Discriminator Loss: 1.2333... Generator Loss: 0.5880
Epoch 1/2... Discriminator Loss: 1.7638... Generator Loss: 0.4890
Epoch 1/2... Discriminator Loss: 1.6167... Generator Loss: 0.5244
Epoch 1/2... Discriminator Loss: 1.0626... Generator Loss: 0.8387
Epoch 1/2... Discriminator Loss: 1.0915... Generator Loss: 0.6341
Epoch 1/2... Discriminator Loss: 1.4857... Generator Loss: 0.4974
Epoch 1/2... Discriminator Loss: 1.5494... Generator Loss: 0.3969
Epoch 1/2... Discriminator Loss: 1.3822... Generator Loss: 0.5356
Epoch 1/2... Discriminator Loss: 1.4499... Generator Loss: 0.5979
Epoch 1/2... Discriminator Loss: 1.2898... Generator Loss: 0.5842
Epoch 1/2... Discriminator Loss: 1.2505... Generator Loss: 0.7737
Epoch 1/2... Discriminator Loss: 1.3077... Generator Loss: 0.5055
Epoch 1/2... Discriminator Loss: 1.2836... Generator Loss: 0.5478
Epoch 1/2... Discriminator Loss: 1.8816... Generator Loss: 0.3531
Epoch 1/2... Discriminator Loss: 1.7349... Generator Loss: 0.5431
Epoch 1/2... Discriminator Loss: 1.5646... Generator Loss: 0.4470
Epoch 1/2... Discriminator Loss: 1.1505... Generator Loss: 0.6866
Epoch 1/2... Discriminator Loss: 1.3113... Generator Loss: 0.5682
Epoch 1/2... Discriminator Loss: 1.4142... Generator Loss: 0.6835
Epoch 1/2... Discriminator Loss: 1.5101... Generator Loss: 0.5923
Epoch 1/2... Discriminator Loss: 1.1162... Generator Loss: 0.6308
Epoch 1/2... Discriminator Loss: 0.5592... Generator Loss: 1.0541
Epoch 1/2... Discriminator Loss: 1.5658... Generator Loss: 0.5584
Epoch 1/2... Discriminator Loss: 1.2065... Generator Loss: 0.6644
Epoch 1/2... Discriminator Loss: 1.5590... Generator Loss: 0.6445
Epoch 1/2... Discriminator Loss: 1.7720... Generator Loss: 0.4142
Epoch 1/2... Discriminator Loss: 1.5608... Generator Loss: 0.5308
Epoch 1/2... Discriminator Loss: 1.6093... Generator Loss: 0.5803
Epoch 1/2... Discriminator Loss: 1.1402... Generator Loss: 0.7327
Epoch 1/2... Discriminator Loss: 2.0902... Generator Loss: 0.3112
Epoch 1/2... Discriminator Loss: 1.3791... Generator Loss: 0.5386
Epoch 1/2... Discriminator Loss: 1.3705... Generator Loss: 0.5973
Epoch 1/2... Discriminator Loss: 1.7018... Generator Loss: 0.6939
Epoch 1/2... Discriminator Loss: 1.5769... Generator Loss: 0.4395
Epoch 1/2... Discriminator Loss: 1.3244... Generator Loss: 0.5601
In [ ]:
batch_size = 2**2
z_dim = 2**3
learning_rate = 0.009
beta1 =  0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [17]:
batch_size = 2**6
z_dim = 2**7
learning_rate = 0.0002
beta1 =  0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
-----verify train inputs:-----
('epoch_count', 1)
('batch_size', 64)
('z_dim', 128)
('learning_rate', 0.0002)
('beta1', 0.5)
('get_batches', <bound method Dataset.get_batches of <helper.Dataset object at 0x7f1b5a186198>>)
('data_shape', (202599, 28, 28, 3))
('data_image_mode', 'RGB')
Epoch 1/1... Discriminator Loss: 4.4896... Generator Loss: 0.0140
Epoch 1/1... Discriminator Loss: 3.8883... Generator Loss: 0.0390
Epoch 1/1... Discriminator Loss: 2.9336... Generator Loss: 0.0910
Epoch 1/1... Discriminator Loss: 4.2650... Generator Loss: 0.0197
Epoch 1/1... Discriminator Loss: 2.3952... Generator Loss: 0.1639
Epoch 1/1... Discriminator Loss: 2.7450... Generator Loss: 0.0759
Epoch 1/1... Discriminator Loss: 1.4155... Generator Loss: 0.7362
Epoch 1/1... Discriminator Loss: 1.8043... Generator Loss: 0.2067
Epoch 1/1... Discriminator Loss: 3.9642... Generator Loss: 0.0248
Epoch 1/1... Discriminator Loss: 1.7048... Generator Loss: 0.2579
Epoch 1/1... Discriminator Loss: 1.6974... Generator Loss: 0.2487
Epoch 1/1... Discriminator Loss: 2.2422... Generator Loss: 0.2136
Epoch 1/1... Discriminator Loss: 2.2579... Generator Loss: 0.2144
Epoch 1/1... Discriminator Loss: 1.9497... Generator Loss: 0.2086
Epoch 1/1... Discriminator Loss: 2.1608... Generator Loss: 0.2187
Epoch 1/1... Discriminator Loss: 1.4210... Generator Loss: 0.4353
Epoch 1/1... Discriminator Loss: 1.8871... Generator Loss: 0.3027
Epoch 1/1... Discriminator Loss: 2.7420... Generator Loss: 0.1711
Epoch 1/1... Discriminator Loss: 1.6942... Generator Loss: 0.3226
Epoch 1/1... Discriminator Loss: 2.9572... Generator Loss: 0.2051
Epoch 1/1... Discriminator Loss: 2.0094... Generator Loss: 0.4165
Epoch 1/1... Discriminator Loss: 3.1110... Generator Loss: 0.2022
Epoch 1/1... Discriminator Loss: 1.2565... Generator Loss: 0.4331
Epoch 1/1... Discriminator Loss: 1.8620... Generator Loss: 0.3343
Epoch 1/1... Discriminator Loss: 1.2736... Generator Loss: 0.5003
Epoch 1/1... Discriminator Loss: 1.1195... Generator Loss: 0.5981
Epoch 1/1... Discriminator Loss: 2.2608... Generator Loss: 0.2441
Epoch 1/1... Discriminator Loss: 2.6695... Generator Loss: 0.2005
Epoch 1/1... Discriminator Loss: 1.4869... Generator Loss: 0.4762
Epoch 1/1... Discriminator Loss: 1.8836... Generator Loss: 0.2956
Epoch 1/1... Discriminator Loss: 1.2194... Generator Loss: 0.6856
Epoch 1/1... Discriminator Loss: 1.6923... Generator Loss: 0.4250
Epoch 1/1... Discriminator Loss: 1.4231... Generator Loss: 0.3798
Epoch 1/1... Discriminator Loss: 2.0285... Generator Loss: 0.3147
Epoch 1/1... Discriminator Loss: 1.6472... Generator Loss: 0.3990
Epoch 1/1... Discriminator Loss: 1.5192... Generator Loss: 0.5058
Epoch 1/1... Discriminator Loss: 0.9890... Generator Loss: 0.6565
Epoch 1/1... Discriminator Loss: 2.0185... Generator Loss: 0.3793
Epoch 1/1... Discriminator Loss: 2.7297... Generator Loss: 0.1822
Epoch 1/1... Discriminator Loss: 1.1417... Generator Loss: 0.6457
Epoch 1/1... Discriminator Loss: 2.5272... Generator Loss: 0.2636
Epoch 1/1... Discriminator Loss: 1.5643... Generator Loss: 0.3819
Epoch 1/1... Discriminator Loss: 1.2610... Generator Loss: 0.4905
Epoch 1/1... Discriminator Loss: 1.4263... Generator Loss: 0.4746
Epoch 1/1... Discriminator Loss: 2.4824... Generator Loss: 0.4880
Epoch 1/1... Discriminator Loss: 1.3632... Generator Loss: 0.5070
Epoch 1/1... Discriminator Loss: 1.1958... Generator Loss: 0.7489
Epoch 1/1... Discriminator Loss: 2.0068... Generator Loss: 0.3763
Epoch 1/1... Discriminator Loss: 2.0420... Generator Loss: 0.3075
Epoch 1/1... Discriminator Loss: 1.5726... Generator Loss: 0.6311
Epoch 1/1... Discriminator Loss: 0.9958... Generator Loss: 0.6024
Epoch 1/1... Discriminator Loss: 1.6461... Generator Loss: 0.5415
Epoch 1/1... Discriminator Loss: 2.6549... Generator Loss: 0.2952
Epoch 1/1... Discriminator Loss: 1.3956... Generator Loss: 0.5892
Epoch 1/1... Discriminator Loss: 1.8753... Generator Loss: 0.4518
Epoch 1/1... Discriminator Loss: 1.1682... Generator Loss: 0.6622
Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.5368
Epoch 1/1... Discriminator Loss: 1.4731... Generator Loss: 0.4588
Epoch 1/1... Discriminator Loss: 1.8218... Generator Loss: 0.4427
Epoch 1/1... Discriminator Loss: 1.4708... Generator Loss: 0.5414
Epoch 1/1... Discriminator Loss: 1.6780... Generator Loss: 0.5324
Epoch 1/1... Discriminator Loss: 1.2060... Generator Loss: 0.6122
Epoch 1/1... Discriminator Loss: 1.1488... Generator Loss: 0.5664
Epoch 1/1... Discriminator Loss: 1.6872... Generator Loss: 0.4610
Epoch 1/1... Discriminator Loss: 1.4188... Generator Loss: 0.5271
Epoch 1/1... Discriminator Loss: 1.6757... Generator Loss: 0.5045
Epoch 1/1... Discriminator Loss: 1.3505... Generator Loss: 0.6154
Epoch 1/1... Discriminator Loss: 1.5584... Generator Loss: 0.5431
Epoch 1/1... Discriminator Loss: 1.6944... Generator Loss: 0.5115
Epoch 1/1... Discriminator Loss: 1.5892... Generator Loss: 0.6065
Epoch 1/1... Discriminator Loss: 1.3719... Generator Loss: 0.7251
Epoch 1/1... Discriminator Loss: 1.8015... Generator Loss: 0.2297
Epoch 1/1... Discriminator Loss: 1.5649... Generator Loss: 0.3907
Epoch 1/1... Discriminator Loss: 1.5713... Generator Loss: 0.4507
Epoch 1/1... Discriminator Loss: 0.8219... Generator Loss: 0.7511
Epoch 1/1... Discriminator Loss: 1.3947... Generator Loss: 0.5673
Epoch 1/1... Discriminator Loss: 1.4352... Generator Loss: 0.7376
Epoch 1/1... Discriminator Loss: 2.4830... Generator Loss: 0.3521
Epoch 1/1... Discriminator Loss: 1.3604... Generator Loss: 0.6204
Epoch 1/1... Discriminator Loss: 1.8012... Generator Loss: 0.4585
Epoch 1/1... Discriminator Loss: 1.2312... Generator Loss: 0.6131
Epoch 1/1... Discriminator Loss: 1.5459... Generator Loss: 0.5349
Epoch 1/1... Discriminator Loss: 1.5690... Generator Loss: 0.6634
Epoch 1/1... Discriminator Loss: 1.3301... Generator Loss: 0.5649
Epoch 1/1... Discriminator Loss: 1.4713... Generator Loss: 0.6139
Epoch 1/1... Discriminator Loss: 1.5994... Generator Loss: 0.6055
Epoch 1/1... Discriminator Loss: 1.0065... Generator Loss: 0.7341
Epoch 1/1... Discriminator Loss: 1.9430... Generator Loss: 0.3834
Epoch 1/1... Discriminator Loss: 1.6722... Generator Loss: 0.5654
Epoch 1/1... Discriminator Loss: 1.7663... Generator Loss: 0.4708
Epoch 1/1... Discriminator Loss: 1.4602... Generator Loss: 0.4720
Epoch 1/1... Discriminator Loss: 1.6167... Generator Loss: 0.4906
Epoch 1/1... Discriminator Loss: 1.4402... Generator Loss: 0.7102
Epoch 1/1... Discriminator Loss: 1.5936... Generator Loss: 0.5955
Epoch 1/1... Discriminator Loss: 0.7269... Generator Loss: 1.1610
Epoch 1/1... Discriminator Loss: 1.2134... Generator Loss: 0.7345
Epoch 1/1... Discriminator Loss: 1.8327... Generator Loss: 0.3923
Epoch 1/1... Discriminator Loss: 1.7029... Generator Loss: 0.4957
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3680       self.stack.append(default)
-> 3681       yield default
   3682     finally:

<ipython-input-17-d89dab10bd11> in <module>()
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-14-77206bdf3c97> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     60                                                          input_z            : batch_z,
---> 61                                                          input_learning_rate: learning_rate})
     62                 if steps % print_every == 0:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 

KeyboardInterrupt: 

During handling of the above exception, another exception occurred:

IndexError                                Traceback (most recent call last)
<ipython-input-17-d89dab10bd11> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

/usr/local/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     75                 value = type()
     76             try:
---> 77                 self.gen.throw(type, value, traceback)
     78                 raise RuntimeError("generator didn't stop after throw()")
     79             except StopIteration as exc:

/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3682     finally:
   3683       if self._enforce_nesting:
-> 3684         if self.stack[-1] is not default:
   3685           raise AssertionError(
   3686               "Nesting violated for default stack of %s objects"

IndexError: list index out of range
In [16]:
batch_size = 2**8
z_dim = 2**9
learning_rate = 0.0002
beta1 =  0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
-----verify train inputs:-----
('epoch_count', 1)
('batch_size', 256)
('z_dim', 512)
('learning_rate', 0.0002)
('beta1', 0.4)
('get_batches', <bound method Dataset.get_batches of <helper.Dataset object at 0x7f1ba92b4240>>)
('data_shape', (202599, 28, 28, 3))
('data_image_mode', 'RGB')
Epoch 1/1... Discriminator Loss: 4.1971... Generator Loss: 0.0307
Epoch 1/1... Discriminator Loss: 3.9140... Generator Loss: 0.0326
Epoch 1/1... Discriminator Loss: 2.7656... Generator Loss: 0.0759
Epoch 1/1... Discriminator Loss: 2.6836... Generator Loss: 0.0796
Epoch 1/1... Discriminator Loss: 2.5035... Generator Loss: 0.1440
Epoch 1/1... Discriminator Loss: 1.3939... Generator Loss: 0.5538
Epoch 1/1... Discriminator Loss: 2.0459... Generator Loss: 0.1835
Epoch 1/1... Discriminator Loss: 1.3014... Generator Loss: 0.4004
Epoch 1/1... Discriminator Loss: 1.3527... Generator Loss: 0.7417
Epoch 1/1... Discriminator Loss: 1.8144... Generator Loss: 0.3185
Epoch 1/1... Discriminator Loss: 2.1564... Generator Loss: 0.2824
Epoch 1/1... Discriminator Loss: 1.4935... Generator Loss: 0.4174
Epoch 1/1... Discriminator Loss: 1.7520... Generator Loss: 0.2983
Epoch 1/1... Discriminator Loss: 1.5609... Generator Loss: 0.3521
Epoch 1/1... Discriminator Loss: 1.5555... Generator Loss: 0.4960
Epoch 1/1... Discriminator Loss: 1.4031... Generator Loss: 0.5695
Epoch 1/1... Discriminator Loss: 1.7551... Generator Loss: 0.3323
Epoch 1/1... Discriminator Loss: 1.9392... Generator Loss: 0.3329
Epoch 1/1... Discriminator Loss: 2.0754... Generator Loss: 0.3947
Epoch 1/1... Discriminator Loss: 1.6469... Generator Loss: 0.4304
Epoch 1/1... Discriminator Loss: 2.8587... Generator Loss: 0.1926
Epoch 1/1... Discriminator Loss: 2.3562... Generator Loss: 0.4339
Epoch 1/1... Discriminator Loss: 1.7112... Generator Loss: 0.4582
Epoch 1/1... Discriminator Loss: 1.7957... Generator Loss: 0.4426
Epoch 1/1... Discriminator Loss: 1.1999... Generator Loss: 0.5566
Epoch 1/1... Discriminator Loss: 1.6897... Generator Loss: 0.4076
Epoch 1/1... Discriminator Loss: 1.3812... Generator Loss: 0.6267
Epoch 1/1... Discriminator Loss: 1.2929... Generator Loss: 0.5524
Epoch 1/1... Discriminator Loss: 1.1021... Generator Loss: 0.7596
Epoch 1/1... Discriminator Loss: 1.9432... Generator Loss: 0.3197
Epoch 1/1... Discriminator Loss: 1.0361... Generator Loss: 0.5876
Epoch 1/1... Discriminator Loss: 1.3938... Generator Loss: 0.5320
Epoch 1/1... Discriminator Loss: 1.4899... Generator Loss: 0.4855
Epoch 1/1... Discriminator Loss: 1.3078... Generator Loss: 0.6020
Epoch 1/1... Discriminator Loss: 1.2250... Generator Loss: 0.7615
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3680       self.stack.append(default)
-> 3681       yield default
   3682     finally:

<ipython-input-16-417fdbd43351> in <module>()
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-14-77206bdf3c97> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     55                                                      input_z            : batch_z,
---> 56                                                      input_learning_rate: learning_rate})
     57 

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:

/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 

KeyboardInterrupt: 

During handling of the above exception, another exception occurred:

IndexError                                Traceback (most recent call last)
<ipython-input-16-417fdbd43351> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

/usr/local/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     75                 value = type()
     76             try:
---> 77                 self.gen.throw(type, value, traceback)
     78                 raise RuntimeError("generator didn't stop after throw()")
     79             except StopIteration as exc:

/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3682     finally:
   3683       if self._enforce_nesting:
-> 3684         if self.stack[-1] is not default:
   3685           raise AssertionError(
   3686               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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